print("Compare two tensors: ",torch.eq(data1,data2)) Output First Tensor: tensor([ 0, 45, 67, 0, 23]) Second Tensor: tensor([ 0, 0, 55, 78, 23]) Compare two tensors: tensor([ True, False, False, False, True]) Working 0 equal to 0 – True 45 equal to 0 – False 67...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/torch/_torch_docs.py at 698eefadddb7c276b381ecb510f4bcc219212c25 · pytorch/pytorch
I compare the two header files and there is no difference. Collaborator leslie-fang-intel commented Jan 18, 2024 @Valentine233 please help to reproduce with Core. Collaborator Valentine233 commented Feb 1, 2024 Reproduced on Core. Sign up for free to join this conversation on GitHub. ...
Args: specgrams (Tensor): Real spectrograms (batch, channel, freq, time) mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param] mask_value (float): Value to assign to the masked columns axis (int): Axis to apply masking on (2 -> frequency...
other (Tensor)– second tensor to compare atol (float, optional)– absolute tolerance. Default: 1e-08 rtol (float, optional)– relative tolerance. Default: 1e-05 equal_nan (bool, optional)– if True, then two NaN s will be considered equal. Default: False Example: >>> torch.allclose...
Does a linear interpolation of two tensors start (given by input) and end based on a scalar or tensor weight and returns the resulting out tensor. outi=starti+weighti×(endi−starti)\text{out}_i = \text{start}_i + \text{weight}_i \times (\text{end}_i - \text{start}_i) o...
# 需要导入模块: import torch [as 别名]# 或者: from torch importarange[as 别名]defplot_wh_methods():# from utils.utils import *; plot_wh_methods()# Compares the two methods for width-height anchor multiplication# https://github.com/ultralytics/yolov3/issues/168x = np.arange(-4.0,4.0...
Compare against the full output from fft(): >>> torch.fft.fft(t) tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j]) Notice that the symmetric element T[-1] == T[1].conj() is omitted. At the Nyquist frequency T[-2] == T[2] is it’s own symmetric pair, and ther...
您的代码中有一个小错误，它会导致所有问题:在内部循环中不增加new_idx。因此，您可以将直接从dataset中提取的元素与self.concat_datasets中相应数据集的第一个元素进行比较。
d[d_idx] = ds_dx * grad_out # TODO: compare structure for x_tensor, d_tensor in zip(x_tensors, j_tensors): if x_tensor.is_sparse: def get_stride(size): dim = len(size) tmp = 1 stride = [0] * dim for i in reversed(range(dim)): stride[i] = tmp ...